What are some legal and regulatory considerations related to bias in AI?

Jul 15, 2024 ... As institutions of higher education turn to AI machine learning and data-driven algorithms to make their work more efficient, a new study ... This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those ... Mar 22, 2024 ... Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. Nov 11, 2022 ... Phoebe sent me a letter. She wants to build a Machine Learning model using a small dataset. Should she try a model with low or high variance ... Defining data bias and the list of common types of data bias in ML: selection bias, overfitting/underfitting, outliers, measurement bias, recall bias, ... Oct 14, 2020 ... The result of this technique is an RF bias-correction model that, when applied to SNODAS, offers improved daily estimates of SWE which can then ... Aug 8, 2023 ... Encord Active offers features to help reduce bias in datasets, allowing you to identify and address any potential biases in your data workflow. Feb 7, 2022 ... We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest ... Jun 8, 2023 ... Bias mitigation in machine learning models · 1. Pre-processing algorithms · 2. In-processing algorithms · 3. Post-processing algorithms.